Robots such as
the PR2 by Willow Garage employ depth sensors for acquiring information
about the shape and geometry of their environment. These sensors
discretely sample the three dimensional space with high spatial
resolution and high update rate and therefore generate large point data
sets. Once these so called point clouds have to be stored on the robot
or transmitted over rate-limited communication channels, the interest in
compressing this kind of data emerges and efficient algorithms for
compressing and communicating point clouds become highly relevant.
Further applications for point cloud compression can be found in the
field of 3D television/conferencing.

In our work we
compress the point distribution by performing a spatial decomposition
based on octree data structures. Furthermore, by correlating and
referencing the currently sampled sensor data to previously sensed and
transmitted point cloud information, temporal redundancy can be detected
and removed from the point cloud data stream. In this context, the
detection of changes within the point data sets is of great importance.
By subsequently analyzing and comparing the octree data structures of
adjacent point clouds, spatial changes in point data can be extracted
and used to successively extend the point clouds at the decoder. In
addition, an entropy coder (range/arithmetic coder) is used for further
removing redundancy from the signals to be transmitted/stored. For more information, please visit pointclouds.org.